An adaptive weight assignment scheme in linear subspace approaches for face recognition

Authors:
Satyanadh Gundimada;Vijayan Asari
Affiliations:
Department of Electrical and Computer Engineering, Old Dominion University, Norfolk;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Year:
2006

Citing 7
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Abstract

A methodology for determining the level of confidence of a sub-region in the overall classification of a given face image affected due to varying expressions, illuminations and partial occlusions is presented in this paper. The technique for obtaining the weights for each individual region of the test image is based on a measure of optical flow between that test image and a face model. Individual image regions or the modules are also assigned additional weights by arranging them in the order of their importance in classification. The approach presented is applicable mainly in scenarios where the number of samples in the training set is too little. A K-nearest neighbor distance measure is used in classifying each module of the test image after dimensionality reduction. A total score is calculated for each training class based on the classification result of each module and its associated weights. Considerable increase in recognition accuracy has been observed for PCA, LDA and ICA based linear subspace approaches when implemented using the proposed technique.